AI Discovery for Operational Management Modernization

As projects grew in scale and complexity, spreadsheet-based management began to reveal its structural limitations. The path to a more integrated environment first required a deep understanding of how the operation worked, in its own terms, its language, and its logic.

• Fragmented management in spreadsheets: Operational control distributed across Excel made information consolidation difficult, limited traceability, and reduced the ability to respond to project variations.

• Lack of systemic vision: The absence of an integrated process model made it complex to identify dependencies between stages, roles, and decision flows, essential information for any systemic redesign.

• Distance between operation and technology: Concepts, terms, and practices from the electrification sector needed to be mapped and translated before any technological solution could be precisely designed.

Squadra conducted a Discovery with intensive support from Genius, adopting an investigative and gradual approach. Instead of proceeding directly to the implementation of a new platform, the project prioritized a deep understanding of the operation as a prerequisite for any design decision.

Genius in Action: AI-Accelerated Discovery

Discovery at the Necessary Speed:

• Genius AI as an Accelerator: Agile processing of interviews and operational data, with automated generation of glossaries, flow mapping, and structuring of user stories, bridging technology and operations.

• Human Curation as a Filter: Squadra's experts adjusted deviations, ensured technical quality, and reinterpreted Genius's outputs within the client's systemic context, ensuring fidelity to the actual business.

• Collaborative Tools: Intelligent notebooks, prototyping environments, and visual platforms transformed organized data into clear narratives, facilitating alignment between leadership, operations, and technical teams.

The Discovery delivered a structured foundation of operational knowledge, documented in artifacts that enable the next steps of the modernization journey. The company now has formalized glossaries, functional flows, and user stories, assets that previously existed only implicitly.

The project demonstrated that digital transformation in technical sectors is not just a matter of technology, but also of organizational literacy. AI accelerated the learning curve and artifact production, but consistent results came from the combination with critical human curation. For engineering companies, the main lesson is that migrating from spreadsheets to integrated systems requires both process redesign and cultural translation of terms, concepts, and decision flows.